Tets Maniwa, Editor in Chief for M&E Tech
Tets has been in many areas within the electronics and the publishing industries. Currently, he is the editor in chief of M&E Tech a website involved with the technologies for media and entertainment. He has a BSEE from the University of California at Berkeley, and an MBA from St. Mary’s … More »
A Model for Justifying More EDA Tools
May 31st, 2010 by Tets Maniwa, Editor in Chief for M&E Tech
One of the overwhelming issues facing the EDA community is the need and desire to increase total sales. One of the greatest hurdles in the ongoing chase to get more seats is the inability to convert the design software budget dollars into new seat licenses. Although most large companies have more than adequate dollars budgeted for software, less than a quarter of the dollars represent new tool acquisitions. The balance of the funds are for maintenance, training, and management functions like parceling out the limited number of seats available.
The inherent value of EDA tools is to provide more automation to the design task, thereby increasing the individual engineer’s productivity. As an example of the value of a tool, design for test tools reduce the time for test development and are able to improve fault coverage over manual methods in the test to over 90 percent of all faults. The tool leads to better test coverage of the design resulting in a higher probability of catching the rare or random errors that make the system fail. So the tools simultaneously reduce engineering time and improve test quality by enhancing internal node observability and controllability. As an added benefit, the window to the internal nodes makes the system debug and integration much easier, due to the availability of the internal state data at the time of failure. So here an additional tool not only improves the risk-performance equation in its intended department, but also aids another group in performing the debugging work.
The EDAC work on ROI justification does a good job of addressing the investment parts of the equation. (See the presentation on the EDAC web page www.edac.org/EDAC/EDACHOME/) The problems with the standard financial models for return on investment (ROI), however, include the lack of a sense of time (ROI equals the average return divided by average investment) and the total lack of connection with the issues that most concern the engineering managers. The managers are most concerned with risk reduction, overall productivity, and net increases in total dollar sales, whereas the standard ROI measures only look at changes in the direct outputs from the investment. The greatest problem in approaching the issue from an investment perspective is the need to quantify the results from a change before the fact.
The EDAC analysis does a very good job of displaying the effects of delays in product release on costs and revenues, but suffers in this regard, because it requires the quantification of risk factors and clear estimates of productivity changes. These are exactly the values that people want to measure, but are also the most difficult values to determine.
In addition, the direct outputs for new tool acquisitions are changes in productivity, a metric that the engineering community abhors because it implies the design task is a quantifiable, fixed process and not the exercise in creativity and skill in design that the engineers say it is. Therefore, the attempts to assign weighting values in the financial analysis to adjust the productivity creates a conflict for the person who will be reporting the numbers. A dramatic increase in productivity implies a large part of what the engineer does can be replaced by a piece of software. A small increase or a decrease in productivity implies the tool is not of great value. Neither of these results is desirable for the EDA community or for the engineer reporting the numbers.
One reason that the financial model breaks down in the ASIC world if that the return on investment depends on more than just the engineering department’s efforts. External factors like market position, pricing, profitability, and product features are all part of the return portion of the equation, but these factors are not in the control of the EDA tool purchase decision maker. The overall history of ASICs has been, unfortunately, that although over 90 percent of all ASICs pass customer specifications on the first pass, less than half go into production. If a new product doesn’t go into production, the return on investment becomes a negative value that has no real relation to the measurement parameters of productivity.
Another reason that the basic financial models break down is the need to factor in some adjustment for risk. The relative productivity changes, as difficult as they are to measure, are much easier to quantify than risk reduction, because the level of risk may have no correlation to any dollar amounts. The addition of a tool may increase the risk due to the down time to learn the tool, or may cause a large enough change in the overall design methodology to expose other missing links in the tool chain. On the other hand, an incremental tool change can reduce the risk by enabling a more complete exploration of the design space, thereby ensuring a successful product design. The risk reduction and productivity improvement are probably the most difficult parameters to quantify in assessing the value of a new tool, and the traditional financial analyses only point out the inability to predict a virtually unmeasurable future result.
As an attempt to address some of the other issues in the valuation of tools, here is a simplified model that combines the traditional financial items like return on investment with some concepts from time to market analyses. The traditional inputs for ROI are the costs for the tools and the savings (in time and money) as a result of the tools. The new model also incorporates the estimated reduction in end-item unit volume and ASP for every month the product release is delayed from the best case schedule. Despite the statement that productivity and risk are hard to quantify, the model generates an ROI number as well as provides a means to evaluate a number of scenarios to bound the relative risk.
The model is in an Excel workbook with three worksheets. The assumptions and variables are entered into the first table called “Inputs”. This passes the data to another worksheet for cost, ROI, and productivity analysis. The final sheet shows the time-to-market effects of the tools purchase, in terms of total design costs, size of market, and product sales. The effects of new tool purchases shows up in the “Impacts” worksheet, where relatively small changes in product development time have significant affect on the company’s sales numbers. The number of variables for contributions to the bottom line are too complex for a general analysis, but are easily available for more detailed analysis within the company doing the design.
All of the inputs for the analysis are available on the first page, and are the details you will need to get from the customer. The values are linked into the following sheets as variables in fairly simple equations. The pages are protected only to keep the formula intact. If you find a better algorithm for the cost/benefit evaluation, please feel free to modify the spreadsheet, by turning protection off and making your changes.
Note the “Costs “ page shows fairly small changes in productivity and a negative ROI for most cases. This is the problem with the traditional measurements, one can’t always find much in the way of good news in productivity or ROI for a standard analysis. If a new tool makes a sufficiently large change in productivity, the ROI eventually goes positive.
By combining the costs data and the effects on the total product life revenues, the model provides a means of identifying the total influence a tool purchase makes on the company’s revenues. In the “Impacts” worksheet, we observe the effects of tool purchases on the release of the target IC. By adjusting costs and delays, a user can also get an estimate for the end-of-life function, which is the cross-over point in a late introduction where revenue goes below some threshold value.
For some scenarios, this cross-over point is before the design is completed, and therefore is a useful early indicator that a design program should be stopped early, rather than expending resources on a money-losing proposition. If the
EDA tool can help a company recover from this situation, then the tool truly is of much higher value to the user than just the change in productivity or some ROI. The value of the tool might be the salvation of a company.
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